Hold the Phone: A Big-Data Conundrum

By Sendhil Mullainathan

July 26, 2014

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CreditCreditMinh Uong/The New York Times

One advantage of being a professor is that you can ramble about your eccentric theories to a captive audience. For example, I often grumble to my graduate students that every time a new iPhone comes out, my existing iPhone seems to slow down. How convenient, I might think: Wouldn’t many business owners love to make their old product less useful whenever they released a newer one? When you sell the device and control the operating system, that’s an option.

This particular conspiracy theory has its adherents. But it is especially eccentric for an economist to entertain because economics argues that this type of strategy may not be as good for the bottom line as it sounds. (Catherine Rampell gave a terrific rundown of the economic arguments around planned-obsolescence and Apple conspiracy theories last October on the Economix blog of The New York Times.)

Apple would not comment on such theories. But there are two simple reasons that planned obsolescence might not maximize profits. First, the legal risk. Second, competition and consumer rationality should combine to thwart this strategy. All a competitor needs to do is to offer a smartphone that doesn’t become a brick as quickly, and more people should buy it.

But these are theoretical arguments. And my experience, though constituting a sample size of one, is empirical.

Generally, my students know enough to ignore my grumbling. But in this instance, Laura Trucco, a Ph.D. student in economics at Harvard, followed a hunch. She wanted to see whether my experience was unique. But how? When people become frustrated with a slow phone, she reasoned, they search Google to figure out what to do about it. So, in theory, data on how often people search for “iPhone slow,” as provided by Google Trends, can measure the frustration globally. (Data for only the United States show similar results.)

Because this data is available weekly, she was able to cross-reference these searches against release dates of new phones. The charts show the results, which are, to say the least, striking. In the top chart, there are six distinct spikes, and they correspond to releases of new iPhones.

At a minimum, this shows that my experience is not unique. Yes, phones feel slower over time as they hold more software and as our expectations of speed increase. But the spikes show that the feeling doesn’t grow gradually; it comes on suddenly in the days after a new phone is released.

Yet that’s all it shows: People suddenly feel that their phone is slowing down. It doesn’t show that our iPhones actually became slower. Imagine that someone points out a buzzing sound in your office. Until then, you hadn’t noticed it. But now you can’t hear anything else. Perhaps this is the digital equivalent of that experience: Hearing about a new release makes you contemplate getting a new and faster phone. And you suddenly notice how slow your old phone is.

To test if this is the reason, we can use an important difference between Apple and Google Android. In Apple’s case, the company sells the device and makes the operating system. In principle, this creates the motive (to sell more devices) and the means (control over the operating systems) to slow down the old phone.

Google has the means (it controls the Android operating system), but not the motive because it doesn’t make money directly from selling new hardware. Conversely, Samsung or other sellers of Android phones have the motive but not the means.

If the perception that your phone is slower is attributable to the psychological effect of hearing about a new release, it should be there for both Android and Apple phones. A new phone of either kind should make you focus on your existing one. The conspiracy theory, however, should apply only to one platform.

The second chart shows searches for “Samsung Galaxy slow.” In this chart, there are no noticeable spikes or anything correlated to the release of new Galaxy phones. Try other types of Android phones, and, similarly, there are no new spikes. This is suggestive, though it’s important to note that new releases of Apple products inevitably draw much more media attention than those of other phones.

Still, if attention on new devices is what makes old ones feel slow, why are the spikes on Apple product release dates, and not when the company announces the new products? In 2008, for example, the iPhone3G was announced a full month before its release. There was a spike at the release, but not at the announcement.

This data has an even more benign explanation. Every major iPhone release coincides with a major new operating system release. Though Apple would not comment on the matter, one could speculate — and many have — that a new operating system, optimized for new phones, would slow down older phones. This could also explain the Samsung-iPhone difference: Because only 18 percent of Android users have the latest operating systems on their phones, whereas 90 percent of iPhone users do, any slowdown from a new operating system would be naturally bigger for iPhones.

The important distinction is of intent. In the benign explanation, a slowdown of old phones is not a specific goal, but merely a side effect of optimizing the operating system for newer hardware. Data on search frequency would not allow us to infer intent. No matter how suggestive, this data alone doesn’t allow you to determine conclusively whether my phone is actually slower and, if so, why.

In this way, the whole exercise perfectly encapsulates the advantages and limitations of “big data.” First, 20 years ago, determining whether many people experienced a slowdown would have required an expensive survey to sample just a few hundred consumers. Now, data from Google Trends, if used correctly, allows us to see what hundreds of millions of users are searching for, and, in theory, what they are feeling or thinking. Twitter, Instagram and Facebook all create what is evocatively called the “digital exhaust,” allowing us to uncover macro patterns like this one.

Second, these new kinds of data create an intimacy between the individual and the collective. Even for our most idiosyncratic feelings, such data can help us see that we aren’t alone. In minutes, I could see that many shared my frustration. Even if you’ve never gathered the data yourself, you’ve probably sensed something similar when Google’s autocomplete feature automatically suggests the next few words you are going to type: “Oh, lots of people want to know that, too?”

Finally, we see a big limitation: This data reveals only correlations, not conclusions. We are left with at least two different interpretations of the sudden spike in “iPhone slow” queries, one conspiratorial and one benign. It is tempting to say, “See, this is why big data is useless.” But that is too trite. Correlations are what motivate us to look further. If all that big data does — and it surely does more — is to point out interesting correlations whose fundamental reasons we unpack in other ways, that already has immense value.

And if those correlations allow conspiracy theorists to become that much more smug, that’s a small price to pay.

SENDHIL MULLAINATHAN is a professor of economics at Harvard.

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A version of this article appears in print on , on Page BU6 of the New York edition with the headline: Hold the Phone: A Big-Data Conundrum. Order Reprints | Today’s Paper | Subscribe